How to Use the Accessibility Checker MCP in LlamaIndex

Index the results of Accessibility Checker into your knowledge base with LlamaIndex.

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Connect Accessibility Checker MCP to LlamaIndex

Create your Vinkius account to connect Accessibility Checker to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Accessibility Checker with LlamaIndex

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Store compliance status for later querying.

When you run `check_corridor_and_passage`, the resulting violation details become part of a searchable knowledge index. This means you can query past runs and get answers grounded in specific, measured API data. Building an RAG application is simple: use LlamaIndex to take the output from our MCP Server and store it alongside your internal documents for context.

Search historical bathroom space requirements.

The `check_bathroom_spaciousness` tool’s results get indexed, letting you ask questions like, 'What were the minimum required dimensions for restroom B in 2021?' You pull answers from actual API data, not just general text. This is key when dealing with evolving building codes. Your index keeps a record of compliance over time.

Query specific ramp and doorway checks.

Running `check_ramp_compliance` populates your knowledge base with exact slope measurements and pass/fail status for landings. Later, you can query this index to verify if the current plan matches historic compliance reports. Similarly, using `check_doorway` adds verifiable opening widths to your searchable data store.

Setup guide

Set up Accessibility Checker MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Accessibility Checker MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Accessibility Checker tools.",
)
response = await agent.run("List recent Accessibility Checker data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Accessibility Checker. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Accessibility Checker MCP in LlamaIndex

The MCP output becomes a core resource for your index. You don't just execute the tool; you make its results part of a unified, queryable knowledge graph.

Yes. Indexing the reports from our Accessibility Checker lets your RAG application combine live API measurements with archived structural documents for comparison.

It handles all critical areas: passages, ramps, bathrooms, and doorways. By running these tools and indexing the outputs, you get a single, comprehensive record set for any building.

It provides highly structured compliance measurements, including minimum clear widths and maximum slope ratios. This precise data is perfect for grounding your knowledge base.

The primary data types are structural dimensions and pass/fail compliance statuses, which are indexed as verifiable facts within the vector store.

Use it with your favorite AI tools

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